






'''simple docstring'''







from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCAmelCase        =							logging.get_logger(__name__)




class   __SCREAMING_SNAKE_CASE       (__A       ):


  """simple docstring"""


  _a  : int      			=     ['''pixel_values''']







  def __init__(			self   ,	UpperCamelCase__ = True   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = PILImageResampling.BICUBIC   ,	UpperCamelCase__ = True   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = True   ,	UpperCamelCase__ = 1 / 255   ,	UpperCamelCase__ = True   ,	UpperCamelCase__ = IMAGENET_DEFAULT_MEAN   ,	UpperCamelCase__ = IMAGENET_DEFAULT_STD   ,	**UpperCamelCase__   ,	):






         """simple docstring"""


         super().__init__(**UpperCamelCase__	)
         a_						     =			size if size is not None else {'shortest_edge': 224}
         a_						     =			get_size_dict(UpperCamelCase__   ,	default_to_square=UpperCamelCase__	)
         a_						     =			crop_size if crop_size is not None else {'height': 224, 'width': 224}
         a_						     =			get_size_dict(UpperCamelCase__   ,	param_name='crop_size'	)

         a_						     =			do_resize
         a_						     =			size
         a_						     =			resample
         a_						     =			do_center_crop
         a_						     =			crop_size
         a_						     =			do_rescale
         a_						     =			rescale_factor
         a_						     =			do_normalize
         a_						     =			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         a_						     =			image_std if image_std is not None else IMAGENET_DEFAULT_STD







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__ = PILImageResampling.BICUBIC   ,	UpperCamelCase__ = None   ,	**UpperCamelCase__   ,	):






         """simple docstring"""


         a_						     =			get_size_dict(UpperCamelCase__   ,	default_to_square=UpperCamelCase__	)
         # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
         if "shortest_edge" in size:
                a_						     =			int((256 / 224) * size['shortest_edge']	)
                a_						     =			get_resize_output_image_size(UpperCamelCase__   ,	size=UpperCamelCase__   ,	default_to_square=UpperCamelCase__	)
                a_						     =			{'height': output_size[0], 'width': output_size[1]}
         if "height" not in size_dict or "width" not in size_dict:
                raise ValueError(
                    f'Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'	)
         return resize(
             UpperCamelCase__   ,	size=(size_dict['height'], size_dict['width'])   ,	resample=UpperCamelCase__   ,	data_format=UpperCamelCase__   ,	**UpperCamelCase__	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__ = None   ,	**UpperCamelCase__   ,	):






         """simple docstring"""


         a_						     =			get_size_dict(UpperCamelCase__	)
         if "height" not in size or "width" not in size:
                raise ValueError(f'Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'	)
         return center_crop(UpperCamelCase__   ,	size=(size['height'], size['width'])   ,	data_format=UpperCamelCase__   ,	**UpperCamelCase__	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__ = None   ,	**UpperCamelCase__   ,	):






         """simple docstring"""


         return rescale(UpperCamelCase__   ,	scale=UpperCamelCase__   ,	data_format=UpperCamelCase__   ,	**UpperCamelCase__	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__ = None   ,	**UpperCamelCase__   ,	):






         """simple docstring"""


         return normalize(UpperCamelCase__   ,	mean=UpperCamelCase__   ,	std=UpperCamelCase__   ,	data_format=UpperCamelCase__   ,	**UpperCamelCase__	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = ChannelDimension.FIRST   ,	**UpperCamelCase__   ,	):






         """simple docstring"""


         a_						     =			do_resize if do_resize is not None else self.do_resize
         a_						     =			resample if resample is not None else self.resample
         a_						     =			do_center_crop if do_center_crop is not None else self.do_center_crop
         a_						     =			do_rescale if do_rescale is not None else self.do_rescale
         a_						     =			rescale_factor if rescale_factor is not None else self.rescale_factor
         a_						     =			do_normalize if do_normalize is not None else self.do_normalize
         a_						     =			image_mean if image_mean is not None else self.image_mean
         a_						     =			image_std if image_std is not None else self.image_std

         a_						     =			size if size is not None else self.size
         a_						     =			get_size_dict(UpperCamelCase__   ,	default_to_square=UpperCamelCase__	)
         a_						     =			crop_size if crop_size is not None else self.crop_size
         a_						     =			get_size_dict(UpperCamelCase__   ,	param_name='crop_size'	)

         a_						     =			make_list_of_images(UpperCamelCase__	)

         if not valid_images(UpperCamelCase__	):
                raise ValueError(
                    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                    'torch.Tensor, tf.Tensor or jax.ndarray.'	)

         if do_resize and size is None:
                raise ValueError('Size must be specified if do_resize is True.'	)

         if do_center_crop and crop_size is None:
                raise ValueError('Crop size must be specified if do_center_crop is True.'	)

         if do_rescale and rescale_factor is None:
                raise ValueError('Rescale factor must be specified if do_rescale is True.'	)

         if do_normalize and (image_mean is None or image_std is None):
                raise ValueError('Image mean and std must be specified if do_normalize is True.'	)

         # All transformations expect numpy arrays.
         a_						     =			[to_numpy_array(UpperCamelCase__	) for image in images]

         if do_resize:
                a_						     =			[self.resize(UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__	) for image in images]

         if do_center_crop:
                a_						     =			[self.center_crop(UpperCamelCase__   ,	UpperCamelCase__	) for image in images]

         if do_rescale:
                a_						     =			[self.rescale(UpperCamelCase__   ,	UpperCamelCase__	) for image in images]

         if do_normalize:
                a_						     =			[self.normalize(UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__	) for image in images]

         a_						     =			[to_channel_dimension_format(UpperCamelCase__   ,	UpperCamelCase__	) for image in images]

         a_						     =			{'pixel_values': images}
         return BatchFeature(data=UpperCamelCase__   ,	tensor_type=UpperCamelCase__	)












'''simple docstring'''







import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class   __SCREAMING_SNAKE_CASE       (__A       ):


  """simple docstring"""


  _a  : List[Any]      			=     ['''image_processor''', '''tokenizer''']
  _a  : List[Any]      			=     '''ViTImageProcessor'''
  _a  : int      			=     ('''CLIPTokenizer''', '''CLIPTokenizerFast''')







  def __init__(			self   ,	UpperCamelCase__=None   ,	UpperCamelCase__=None   ,	**UpperCamelCase__	):






         """simple docstring"""


         a_						     =			None
         if "feature_extractor" in kwargs:
                warnings.warn(
                    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                    ' instead.'   ,	UpperCamelCase__   ,	)
                a_						     =			kwargs.pop('feature_extractor'	)

         a_						     =			image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
                raise ValueError('You need to specify an `image_processor`.'	)
         if tokenizer is None:
                raise ValueError('You need to specify a `tokenizer`.'	)

         super().__init__(UpperCamelCase__   ,	UpperCamelCase__	)







  def __call__(			self   ,	UpperCamelCase__=None   ,	UpperCamelCase__=None   ,	UpperCamelCase__=None   ,	UpperCamelCase__=None   ,	**UpperCamelCase__	):






         """simple docstring"""


         if text is None and visual_prompt is None and images is None:
                raise ValueError('You have to specify either text, visual prompt or images.'	)

         if text is not None and visual_prompt is not None:
                raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.'	)

         if text is not None:
                a_						     =			self.tokenizer(UpperCamelCase__   ,	return_tensors=UpperCamelCase__   ,	**UpperCamelCase__	)

         if visual_prompt is not None:
                a_						     =			self.image_processor(UpperCamelCase__   ,	return_tensors=UpperCamelCase__   ,	**UpperCamelCase__	)

         if images is not None:
                a_						     =			self.image_processor(UpperCamelCase__   ,	return_tensors=UpperCamelCase__   ,	**UpperCamelCase__	)

         if visual_prompt is not None and images is not None:
                a_						     =			{
                    'pixel_values': image_features.pixel_values,
                    'conditional_pixel_values': prompt_features.pixel_values,
                }
                return encoding
         elif text is not None and images is not None:
                a_						     =			image_features.pixel_values
                return encoding
         elif text is not None:
                return encoding
         elif visual_prompt is not None:
                a_						     =			{
                    'conditional_pixel_values': prompt_features.pixel_values,
                }
                return encoding
         else:
                return BatchEncoding(data=dict(**UpperCamelCase__	)   ,	tensor_type=UpperCamelCase__	)







  def    _a       (			self   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         return self.tokenizer.batch_decode(*UpperCamelCase__   ,	**UpperCamelCase__	)







  def    _a       (			self   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         return self.tokenizer.decode(*UpperCamelCase__   ,	**UpperCamelCase__	)







  @property
  def    _a       (			self	):






         """simple docstring"""


         warnings.warn(
             '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'   ,	UpperCamelCase__   ,	)
         return self.image_processor_class







  @property
  def    _a       (			self	):






         """simple docstring"""


         warnings.warn(
             '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'   ,	UpperCamelCase__   ,	)
         return self.image_processor




